Deep Learning


Deep Learning (also known as deep structured learning or hierarchical learning) is part of a broader family of Machine Learning methods based on learning data representations, as opposed to task-specific algorithms.

Deep Learning can be Supervised Learning, partially supervised or unsupervised Learning.

Deep Learning architectures such as Deep Neural networks, Deep Belief networks and Recurrent Neural networks have been applied to fields including computer vision, speech recognition, Natural Language Processing, Sound recognition, social Websites filtering, machine translation, bioinformatics and drug design, where they have produced results comparable to and in some cases superior to human experts.

Deep Learning is a class of Machine Learning algorithms that:

  • use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input.
  • learn in supervised (e.g., classification) and/or Unsupervised Learning (e.g., pattern analysis) manners.
  • learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts.
  • use some form of gradient descent for training via backpropagation.
Layers that have been used in Deep Learning include hidden nodes of an Artificial Neural network and sets of propositional formulas.

Deep Learning may also include latent variables organized layer-wise in deep generative models such as the nodes in Deep Belief Networks and Deep Boltzmann Machines.


Artificial Intelligence

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